Abstract
Assessment of mind wandering commonly entails the use of experience sampling to capture episodes in the midst of their occurrence. Time series sequences of responses to experience sampling probes have been shown to exhibit consequential temporal dynamics, including power-law scaling behavior in their fluctuations across increasing time scales. This scaling behavior implies long-range temporal correlations in the time series, in which mind wandering occurring far in the past can exert some influence on future episodes over increasingly large temporal intervals. Yet, it is known that short-range correlated Markov processes can also exhibit similar scaling behavior, making it critical that apparent long-range temporal correlations in time series measurements of mind wandering are evaluated with respect to short-range correlated null hypotheses. In the present study, we examine long-range temporal correlations in sequences of mind wandering probe ratings as well as in simulated sequences generated by short-range correlated Markov processes. To evaluate whether mind wandering probe ratings display genuine long-range temporal dependence, we contrast patterns of autodependence in mind wandering probe rating data with confidence intervals derived from short-range correlated Markov models and sequences temporally permuted to remove any temporal structure. We find that autodependence generally extended over short and intermediate ranges but was undifferentiated from short-range correlated processes later in time. This suggests that sequences of mind wandering probe ratings on average have finite memory with dynamics operating at shorter-range characteristic scales beyond which autodependence decays to that of a short-range correlated process.
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Data Availability Statement
The conclusions of this study are based on secondary analyses of data originally shared online by Irrmischer et al., (2018; https://figshare.com/articles/dataset/data/6027011), Krimsky et al., (2017; https://doi.org/10.1016/j.cognition.2017.08.006), Zanesco et al., (2020; https://osf.io/rf9jh/), and Kane et al., (2016; https://osf.io/guhw7/). Analysis scripts and curated data files used in this study are also provided online in the OSF repository and can be found at: https://osf.io/gtfa8/.
Notes
That the simulated hidden Markov sequences show greater overall Sρ than the mean of the empirical probe ratings may reflect differences in the overall amount of entropy in the sequences. This is because some empirical sequences had fewer numbers of unique rating categories than others, whereas the HMM had some probability of generating data from every probe rating category. For example, the mean normalized entropy of 131 simulated sequences (mean entropy = 0.682, SD = 0.201) was greater than the mean of the 131 empirical sequences (mean entropy = 0.568, SD = 0.231). In contrast to the HMM, surrogates generated from transition probabilities of each original sequence will differ less in overall entropy because new states are not introduced into the sequence that were not originally present and the probability distribution of ratings is mostly preserved. Alternatively, HMM surrogates may genuinely overestimate the amount of non-linear serial dependence in the original data.
Ten additional participants (total permuted n = 344) were excluded from these analyses because their Hurst exponent was outside the range of 0 to 2 after permutation of their probe ratings.
DFA of categorical time series requires partitioning the categorical ratings so that the occurrence of some of the categories drives the random walk in a positive direction and the occurrence of the remaining categories drives the walk in a negative direction. Thus, the choice of categorical embedding affects fluctuations in the integrated random walk and resultant Hurst scaling exponents. For categorical data with more than two categories, there is often no clear rationale for deciding which categories should be partitioned in this manner. In time series of categorical electrophysiological brain states (i.e., EEG microstates; Michel & Koenig, 2018), DFA has been separately conducted on all unique bipartitions of categories (Van De Ville, Britz, & Michel, 2010; von Wegner et al., 2018). In contrast, there is a clear theoretical rationale in the present case for dichotomizing categorical thought probe ratings into on-task (ratings of (1) “on-task” and (2) “task-related thoughts”) and off-task categories (ratings 3 through 8) based on how these ratings are commonly partitioned for analyses in prior studies.
Four additional participants (total permuted n = 459) were excluded from these analyses because their Hurst exponent was outside the range of 0 to 2 after permutation of their probe ratings.
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Acknowledgements
We thank Marissa Krimsky, Emily Brudner, Keith Chichester, Sydney Feldman, and Ariel Paz for their assistance with data collection. We also thank Mona Irrmischer, Natalie van der Wal, Huibert Mansvelder, and Klaus Linkenkaer-Hansen, and Michael Kane, Matt Meier, Bridget Smeekens, Georgina Gross, Charlotte Chun, Paul Silvia, Matthew Welhaf, Nicholas Gazzia, Joshua Perkins, and Thomas Kwapil, for sharing their deidentified data online.
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APZ was involved in the conceptualization and interpretation of the study, formal analysis, and writing of the manuscript. ED was involved in interpretation of study results and writing of the manuscript. APJ was involved in interpretation of study results and writing of the manuscript. All authors have read and approved the manuscript.
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Zanesco, A.P., Denkova, E. & Jha, A.P. Examining Long-Range Temporal Dependence in Experience Sampling Reports of Mind Wandering. Comput Brain Behav 5, 217–233 (2022). https://doi.org/10.1007/s42113-022-00130-9
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DOI: https://doi.org/10.1007/s42113-022-00130-9